1. Field of the Invention
This disclosure generally relates to data filters for modeling and processing credit report data and other data, and more particularly to improved systems and methods for generating and using data filters configured to conduct customer profiling and customer analysis relating to modeling, identifying, and/or predicting the never-pay population.
2. Description of the Related Art
Various financial service entities provide credit accounts, such as, for example, mortgages, automobile loans, credit card accounts, and the like, to consumers and or businesses. Prior to providing a credit account to an applicant, or during the servicing of such a credit account, many financial service providers want to know whether the applicant or customer will be or is likely to be within the “never-pay” population. The never-pay population includes without limitation those customers that make a request for credit, subsequently obtain the credit instrument, and over the life of the account, never make a payment or substantially never make a payment. Although the never-pay population is not always large (however, it can be a large population for certain financial firms, for example, those firms serving the sub-prime market or the like), it is a costly population to financial service providers and other entities. Most financial service providers can attribute a certain percentage of their losses to the never-pay population.
Traditional scoring models do not provide the necessary insight to identify the never-pay population. In part, this is due to the diversity of profiles that underlie these populations. Additionally, the attributes and/or reasons that contribute to the never-pay population are difficult to identify for some financial service providers because of their limited resources and the complexity of analyzing the never-pay population. Accordingly, these never-pay accounts are not identified early in the process, and are treated as typical credit loss and are often written off as bad debt.